What if wireless routers were social? approaching wireless mesh networks from a social networks perspective

Kas, Miray; Appala, Sandeep; Wang, C.; Carley, Kathleen M.; Carley, L.R.; Tonguz, O.K.

doi:10.1109/mwc.2012.6393516

Cited by 24 publications

(13 citation statements)

References 16 publications

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“…Kas et al [23] studied social network analysis metrics and centrality measures as tools to improve the design of wireless mesh networks with better performance by efficiently allocating the resources available. The allocation of network resources using the centrality measures as heuristics has been a topic of study in distributed placement of autonomic Internet services for the efficient support of users' demands [24].…”

Section: Centrality In Computer Networkmentioning

confidence: 99%

Machine Learning in Network Centrality Measures

2018

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Complex networks are ubiquitous to several Computer Science domains. Centrality measures are an important analysis mechanism to uncover vital elements of complex networks. However, these metrics have high computational costs and requirements that hinder their applications in large real-world networks. In this tutorial, we explain how the use of neural network learning algorithms can render the application of the metrics in complex networks of arbitrary size. Moreover, the tutorial describes how to identify the best configuration for neural network training and learning such for tasks, besides presenting an easy way to generate and acquire training data. We do so by means of a general methodology, using complex network models adaptable to any application. We show that a regression model generated by the neural network successfully approximates the metric values and therefore are a robust, effective alternative in real-world applications. The methodology and proposed machine learning model use only a fraction of time with respect to other approximation algorithms, which is crucial in complex network applications.

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Section: Centrality In Computer Networkmentioning

confidence: 99%

Machine Learning in Network Centrality Measures

2018

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“…According to Kas et al in [34], when nodes with high betweenness centrality die, the number of hops between any two nodes in a network becomes greater. This happens because the high betweenness nodes are on a high fraction of shortest paths among other nodes in the network.…”

Section: Related Workmentioning

confidence: 99%

A Cooja-Based Tool for Coverage and Lifetime Evaluation in an In-Building Sensor Network

Sitanayah

Sreenan

Fedor

2016

JSAN

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Abstract:Contiki's Cooja is a very popular wireless sensor network (WSN) simulator, but it lacks support for modelling sensing coverage, focusing instead on network connectivity and protocol performance. However, in practice, it is the ability of a sensor network to provide a satisfactory level of coverage that defines its ultimate utility for end-users. We introduce WSN-Maintain, a Cooja-based tool for coverage and network lifetime evaluation in an in-building WSN. To extend the network lifetime, but still maintain the required quality of coverage, the tool finds coverage redundant nodes, puts them to sleep and automatically turns them on when active nodes fail and coverage quality decreases. WSN-Maintain together with Cooja allow us to evaluate different approaches to maintain coverage. As use cases to the tool, we implement two redundant node algorithms: greedy-maintain, a centralised algorithm, and local-maintain, a localised algorithm to configure the initial network and to turn on redundant nodes. Using data from five real deployments, we show that our tool with simple redundant node algorithms and reading correlation can improve energy efficiency by putting more nodes to sleep.

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“…For performance evaluation both utilities has been assigned the same importance. For their part, authors in [22] apply social network analysis metrics to detect critical…”

Section: Related Workmentioning

confidence: 99%

A centrality-based topology control protocol for wireless mesh networks

Vázquez-Rodas¹,

Llopis²

2015

Ad Hoc Networks

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Nodes in wireless multi-hop networks establish links with their neighbors, which are used for data transmission. In general, in this kind of networks every node has the possibility of acting as a router, forwarding the received packets when they are not the final destination of the carried data. Due to the routing protocol procedures, when the network is quite dense the overload added by the routing management packets can be very high. To reduce the effects of this overload a topology control mechanism can be used, which can be implemented using different techniques. One of these techniques consists of enabling or disabling the routing functionality in every node. Many advantages result from selecting just a subset of nodes for routing tasks: reduction of collisions, protocol overhead, interference and energy consumption, better network organization and scalability. In this paper, a new protocol for topology control in wireless mesh networks is proposed. The protocol is based on the centrality metrics developed by social network analysts. Our target network is a wireless mesh network created by user hand-held devices. For this kind of networks, we aim to construct a connected dominating set that includes the most central nodes. The resulting performance using the three most common centrality measures (degree, closeness and betweenness) is evaluated. As we are working with dynamic and decentralized networks, a distributed implementation is also proposed and evaluated. Some simulations have been carried out to analyze the benefits of the proposed mechanism when reactive or proactive routing protocols are used. The results confirm that the use of the topology control contributes to a better network performance.

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What if wireless routers were social? approaching wireless mesh networks from a social networks perspective

Cited by 24 publications

References 16 publications

Machine Learning in Network Centrality Measures

Machine Learning in Network Centrality Measures

A Cooja-Based Tool for Coverage and Lifetime Evaluation in an In-Building Sensor Network

A centrality-based topology control protocol for wireless mesh networks

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